



'''simple docstring'''





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__          =  get_tests_dir("fixtures/test_sentencepiece.model")
SCREAMING_SNAKE_CASE__          =  get_tests_dir("fixtures/test_sentencepiece_bpe.model")

SCREAMING_SNAKE_CASE__          =  'pt' if is_torch_available() else 'tf'


@require_sentencepiece
@require_tokenizers
class 					snake_case			(lowerCamelCase__		,   unittest.TestCase ):
       lowerCAmelCase__	:Tuple     =       CamembertTokenizer
       lowerCAmelCase__	:int     =       CamembertTokenizerFast
       lowerCAmelCase__	:Dict     =       True
       lowerCAmelCase__	:Any     =       True



       def 				_a   (					self   )	->				Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase__									=    CamembertTokenizer(__snake_case   )
           tokenizer.save_pretrained(self.tmpdirname   )



       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    '<pad>'
           lowercase__									=    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case   )  ,__snake_case   )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case   )  ,__snake_case   )



       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    list(self.get_tokenizer().get_vocab().keys()   )

           self.assertEqual(vocab_keys[0]  ,"<s>NOTUSED"   )
           self.assertEqual(vocab_keys[1]  ,"<pad>"   )
           self.assertEqual(vocab_keys[-1]  ,"<mask>"   )
           self.assertEqual(len(__snake_case   )  ,1_004   )



       def 				_a   (					self   )	->				Dict:
           self.assertEqual(self.get_tokenizer().vocab_size  ,1_005   )



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    CamembertTokenizer(__snake_case   )
           tokenizer.save_pretrained(self.tmpdirname   )
           lowercase__									=    CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

           lowercase__									=    'I was born in 92000, and this is falsé.'

           lowercase__									=    tokenizer.encode(__snake_case   )
           lowercase__									=    rust_tokenizer.encode(__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )

           lowercase__									=    tokenizer.encode(__snake_case  ,add_special_tokens=__snake_case   )
           lowercase__									=    rust_tokenizer.encode(__snake_case  ,add_special_tokens=__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )

           # <unk> tokens are not the same for `rust` than for `slow`.
           # Because spm gives back raw token instead of `unk` in EncodeAsPieces
           # tokens = tokenizer.tokenize(sequence)
           lowercase__									=    tokenizer.convert_ids_to_tokens(__snake_case   )
           lowercase__									=    rust_tokenizer.tokenize(__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )



       def 				_a   (					self   )	->				Optional[int]:
           if not self.test_rust_tokenizer:
               return

           lowercase__									=    self.get_tokenizer()
           lowercase__									=    self.get_rust_tokenizer()

           lowercase__									=    'I was born in 92000, and this is falsé.'

           lowercase__									=    tokenizer.tokenize(__snake_case   )
           lowercase__									=    rust_tokenizer.tokenize(__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )

           lowercase__									=    tokenizer.encode(__snake_case  ,add_special_tokens=__snake_case   )
           lowercase__									=    rust_tokenizer.encode(__snake_case  ,add_special_tokens=__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )

           lowercase__									=    self.get_rust_tokenizer()
           lowercase__									=    tokenizer.encode(__snake_case   )
           lowercase__									=    rust_tokenizer.encode(__snake_case   )
           self.assertListEqual(__snake_case  ,__snake_case   )







       @slow
       def 				_a   (					self   )	->				Dict:
           # fmt: off
           lowercase__									=    {'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # camembert is a french model. So we also use french texts.
           lowercase__									=    [
               'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
               'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
               'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
               'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
               'telles que la traduction et la synthèse de texte.',
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=__snake_case  ,model_name="camembert-base"  ,revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,sequences=__snake_case  ,)









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """Salesforce/blip-image-captioning-base"""
    lowercase__					=       (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    lowercase__					=       """image_captioner"""
    lowercase__					=       AutoModelForVisionaSeq

    lowercase__					=       ["""image"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    Tuple		,     *__snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      requires_backends(self		,     ['vision']      )
      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    "Image"      ):
      return self.pre_processor(images=__snake_case		,     return_tensors='pt'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str      ):
      return self.model.generate(**__snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Tuple      ):
      return self.pre_processor.batch_decode(__snake_case		,     skip_special_tokens=__snake_case      )[0].strip()